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Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imaging by Qu, Wendi; Balki, Indranil; Mendez, Mauro; Valen, John; Levman, Jacob; Tyrrell, Pascal N. is a Medicine article available to read on EtoBox.

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## Purpose: Machine learning (ml) algorithms are well known to exhibit variations in prediction accuracy when provided with imbalanced training sets typically seen in medical imaging (mi) due to the imbalanced ratio of pathological and normal cases. this paper presents a thorough investigation of the effects of class imbalance and methods for mitigating class imbalance in ml algorithms applied to mi. ## Methods: We first selected five classes from the image retrieval in medical applications (irma) dataset, performed multiclass classification using the random forest model (rfm), and then performed binary classification using convolutional neural network (cnn) on a chest x-ray dataset. an imbalanced class was created in the training set by varying the number of images in that class. methods tested to mitigate class imbalance included oversampling, undersampling, and changing class weights of the rfm. model performance was assessed by overall classification accuracy, overall f1 score, and specificity, recall, and precision of the imbalanced class. ## Results: A close-to-balanced training set resulted in the best model performance, and a large imbalance with overrepresentation was more

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Author

Qu, Wendi; Balki, Indranil; Mendez, Mauro; Valen, John; Levman, Jacob; Tyrrell, Pascal N.

Publisher

Springer-Verlag; Springer Verlag; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1861-6410)

Published

2020

Language

EN

Field

Medicine (Physical Sciences)